Benchmark testing
Your model's outputs measured against the accuracy targets you define, on a test set that looks like your real users.
For go or no-go decisions before launch
Evaluation for AI teams
Data and model evaluation, run on your model's outputs
Data and model evaluation is benchmark testing against accuracy targets you set, plus bias and fairness checks across groups and languages, and monitoring after your model ships. We check the outputs and the data behind them. Your team trains the model, and we tell you how well it's doing.
group vs your target
- overall on target
- Indian English on target
- Hindi on target
- Tamil speakers below target
overall looks fine. one group doesn't.
! gap flagged in the report, with a re-check plan
The short answer
What is data and model evaluation?
Data and model evaluation checks how well a trained model performs, and whether the data behind it can be trusted. The model's outputs are compared with checked answers, against targets you set, and the results are broken down by the groups your users belong to.
It's separate from training. Your team builds and trains the model, and evaluation measures it from the outside, before launch and on a schedule afterward.
Good evaluation depends on the test set as much as the model. It should look like your real users and stay separate from the training data. If it doesn't, the scores flatter you.
An evaluation is only as good as the labels behind it, so it helps to read how annotation quality is judged before you set a target.
Evaluation at IndecalAI
- Checks
- Benchmarks against your accuracy targets, bias and fairness checks, and monitoring after launch.
- Groups
- Results compared across demographic and linguistic groups, languages and accents included.
- Data
- Training and test data checked before it gets anywhere near a model.
- Models
- We don't train, tune or ship models. Your team trains its own, and we check it.
- People
- Human judgment from a network of 30,000+ people worldwide.
- Languages
- Languages worldwide, with deep coverage of Indian languages well past Hindi and English.
- Results
- Findings your team can act on, and a plan for checking again.
- Your data
- NDA first, used only for the agreed scope, returned or deleted when you ask.
- Contact
- One named contact who replies within one business day.
- First step
- A small free sample before you commit.
- Company
- Based in Ahmedabad, India, since 2021, with 150+ projects delivered.
What we check
Six checks on your model and its data
Your model's outputs get measured against the accuracy targets you define and compared across demographic and linguistic groups. After launch we keep checking on a schedule. And we can look at the training or test data itself, before it gets anywhere near a model.
Bias and fairness checks
Results compared across demographic groups to find the people your model serves worst, before they find out for themselves.
For fair treatment across the people who use it
Across languages and accents
Language is one of the groups we compare, including speech that mixes two languages, which is where models often slip.
For multilingual assistants and speech recognition
Post-launch monitoring
Models drift as users pick up new slang and new products launch. A fresh sample of outputs gets checked against human judgment on a schedule we agree.
For catching a drop before your customers do
Training and test data checks
The data itself, checked before it gets near a model: whether the test set looks like your users, and whether it's truly separate from training.
For scores you can believe
Human judgment of outputs
Where there's no single right answer, like a chatbot reply, people who know the language and the domain rate each output against rules agreed with you.
For chatbots, assistants and generated text
Where it's used
What evaluation tells your team
Common reasons teams check a model, and the part of this page behind each one. They're examples of the work, not a client list.
Go or no-go before launch
A clear answer on whether the model meets the targets you set, on data that looks like your users.
Finding who the model fails
Results split by age, gender, region or other groups, so a gap hiding in the average comes out.
Voice bots across accents
The same bot checked for each language and accent your callers have, mixed speech included.
Catching drift after launch
Regular checks on fresh outputs, so a slow slide in quality gets noticed before customers complain.
Trusting the test set
A check that test items never appeared in training, and that the test set looks like real use.
Choosing between two versions
Two versions of your model run on the same test set, so the better one wins on evidence.
Why averages mislead
One accuracy number hides a lot
An overall accuracy figure is an average. A speech model can score well overall and still fail badly for one accent, one age group or one language, because the groups it handles well outweigh the ones it doesn't.
So we compare results across groups and languages, and we care about the test set as much as the model. Language is one of the groups we compare, and we work in languages from all over the world, with deep coverage of Indian ones (see Indian language data).
Before testing starts
Settle these first
Evaluation is only as useful as the questions it answers. These four get settled with you at the start.
What good looks like
The accuracy targets the model has to meet, in numbers where possible, and what counts as a correct answer.
Which groups matter
The demographic and linguistic groups your users belong to, so the results get broken down the way your risks are.
A test set you can trust
Test data that looks like your real users and stays apart from the training data. Otherwise the scores flatter you.
How often to check again
Models drift after launch. We agree the schedule with you and report back on it each time.
How it works
From your targets to findings you can act on
Your named contact stays with it from the first target to the last re-check. Here's the usual order.
Targets
You tell us what good looks like, in numbers where possible.
NDA first
The NDA is signed before any of your data moves.
Test set and groups
We agree the test data, the correct answers and the groups that matter.
Free sample
We evaluate a small sample free, so you can see the kind of findings you get.
Testing
Outputs and data get tested against your targets, across groups and languages.
Findings and re-checks
You get findings your team can act on, and a plan for checking again.
Glossary
The vocabulary of evaluation
The words that come up when you plan an evaluation, and what each one means.
- Benchmark
- A fixed test the model runs against, with agreed correct answers and target scores, so results can be compared over time.
- Test set
- Data kept aside to measure the model, never used to train it. It should look like the people who'll use the model.
- Ground truth
- The answers checked by people that model outputs get compared with. If the ground truth is wrong, so is every score.
- Data leakage
- When test items also appear in the training data. The model has seen the answers, and the scores look better than they are.
- Subgroup analysis
- Splitting results by group, such as age, accent or language, to find where a model underperforms.
- Model drift
- A slow drop in quality after launch, because the language, products or users have changed since the model was trained.
- Precision and recall
- Precision is how many of the model's answers were right. Recall is how many of the right answers it found.
- Human evaluation
- People rating model outputs against agreed rules, used where there's no single correct answer to compare with.
Related work
Where evaluation fits in
One team can collect the test data, label it and check your model against it. You can also compare all eight services.
Data collection
New test data gathered from the people your model will serve.
Data annotation and labeling
Checked labels to use as the correct answers.
Transcription
Reviewed transcripts to score speech models against.
Conversational AI data
Real conversations to test a voice bot or assistant with.
Synthetic data
Generated data for gaps, always tested on real data too.
FAQ
Questions about evaluation
Short answers about this service. The services FAQ covers the other services and how they fit together.
What is data and model evaluation?
Checking how well a trained model performs, and whether the data behind it can be trusted. The model's outputs are compared with checked answers, against targets you set, and the results are broken down by the groups your users belong to.
Do you train or fine-tune our model?
No. We provide data and evaluation. You train your own model, and we check its outputs.
Why isn't one overall accuracy number enough?
It's an average. A model can score well overall and still fail badly for one accent, one age group or one language, because the groups it handles well outweigh the ones it doesn't. So we compare results across groups.
Can you check for bias across languages and accents?
Yes. Bias and fairness checks across demographic and linguistic groups are part of the service.
What do you need from us to start?
Your model's outputs, and agreement on what a correct answer looks like. No accuracy targets yet? That's a good first conversation to have with us.
How often should a model be checked after launch?
It depends on how fast your users, products and language change. We agree a schedule with you, check a fresh sample of outputs against human judgment each time, and report back.
Can we try a small sample first?
Yes. Send a short brief and we'll evaluate a small sample free before you commit.
Does our data stay private?
Yes. The NDA is signed before any data moves, we use it only for the scope we agreed, and only the people working on your project can get to it. It's returned or deleted when you ask.
Start with a free sample
Pick the checks you need, say what your model does and which groups matter, and send it. We'll evaluate a small sample free before you commit.
- Your named contact replies within one business day.
- We sign an NDA before any data changes hands.
- We evaluate your free sample, then agree the full scope.
This opens your email app with the brief filled in. Nothing is sent until you press send. You can also use the contact page.